Comprehensive DoE capabilities for Synthesis, Process Development and Manufacturing of Novel Chemical Entities (NCE's)

The Design of Experiments (DoE) methodology integrates mathematical and statistical techniques to create efficient, balanced, and cost-effective experimental designs. These designs enable researchers to discern the relationship between controlled and uncontrolled factors and process outputs. Applied across product/process design, development and manufacturing processes, DoE is a potent tool for optimizing processes, enhancing product quality, and enhancing efficiency during scale-up and large-scale manufacturing or conducting the design of experiments for all client projects related to discovery chemistry, process research, development, chemical development solutions, as well as chemical manufacturing solutions for synthesizing, scaling-up, and manufacturing novel chemical entities, including pharmaceutical candidates, fine chemicals, specialty chemicals, APIs, and biological products. Our DoE experts have designed efficient strategies to provide higher yields within short turnaround time and lower expenses.

Aragen has a dedicated scientific team for conducting the design of experiments for all client projects related to discovery chemistry, process research, development, chemical development solutions, as well as chemical manufacturing solutions for synthesizing, scaling-up, and manufacturing novel chemical entities, including pharmaceutical candidates, fine chemicals, specialty chemicals, APIs, and biological products. Our DoE experts have designed efficient strategies to provide higher yields within short turnaround time and lower expenses.

• Changing multiple factors at the same time to study the impact using less number of experiments.
• Provides accurate information to design and develop new processes and products, thus minimizing the time and resource requirements.
• Deriving an understanding of multiple factors interaction unlike OFAT study where one factor interaction is studied.
• Enhancing product and process robustness.
• Helps to understands possible variabilities and reducing process.
• To avoid fluctuations or variability.
• Maximizing quality and profitability of product